Evaluating and grading meat animals, both live and slaughtered, has historically been performed by humans. Because of this it is very difficult to achieve accuracy, efficiency and consistency. Both producers and packers demand an objective means of classifying their animals accurately according to their carcass real values. However, since an accurate, quick, and consistent grading system has not been put into place, producers are not being paid for the true value of their animals. Currently, producers are paid on an average basis. The price differential between a high-yield and a low-yield grade is less than it should be. Therefore, it is important to the hog and beef industries that improved or new technologies must be developed in their evaluation systems in order to be able to accurately measure the hog and beef carcass characteristics that are of significant value.
The ultrasonic equipment currently used in practice is either A-mode (A for amplitude) or B-mode (B for brightness). The A-mode scanner does not produce a two-dimensional image but the B-mode does. Hence, most ultrasonic scanners in medical and animal fields are B-mode. In practical application, the images produced by the B-scanner, in the animal field for quality (e.g. marbling) and quantity (e.g. fat depth and muscle area) evaluation are currently assessed by human eyes. There is no objective tool or computer software or hardware currently available to automate the evaluation process.
Currently, ultrasonic images are interpreted and evaluated by humans. Only highly trained interpreters can analyze these images to determine the fat thickness and muscle area. This is not only a time consuming and laborious process but also a subjective method from which accuracy, repeatability, and efficiency can hardly be achieved. In previous studies attempts were made to develop computer algorithms for ultrasound image analysis to estimate important carcass traits in both live animals and carcasses.
Typically, ultrasonic images of the Longissimus dorsi (rib eye muscle in beef and loin eye muscle in hogs) have been used to evaluate livestock. This has been done by positioning the transducer in either a perpendicular or parallel direction with respect to the backbone of the livestock. EPO Patent application publication number 0 337 661 A1, entitled, "Method and apparatus for grading of live animals and animal carcasses" teaches method and apparatus for longitudinal (parallel to the backbone) scanning and image recognition to determine automatically fat and muscle characteristics. Wilson was not the first to use longitudinal scanning to evaluate carcasses, as shown by the Phd thesis by Wayne A. Gillis entitled "An Evaluation of Indices of Carcass Yield, Physical Composition and Chemical Composition in Swine; and Ultrasonic Measurement of the Longissimus Dorsi Area and Fat Thickness in Beef and Swine", which shows longitudinal scanning Another group currently using longitudinal scanning is CSB-SYSTEM of America Corporation.
Neither Wilson, Gillis or CSB teach some of the problems associated with performing longitudinal scans or method or apparatus for consistently locating the transducer on the animal or carcass. One problem with longitudinal scanning occurs when the transducer is parallel to the back fat layers. Artifacts or multiples of the fat layers and the muscle/fat interface show up down in the image of the muscle layer. These multiples occur as a result of the sound waves rebounding directly back off of these layers and interfere with image recognition apparatus methods for determining muscle and fat composition. As can be seen by the advertising literature, the CSB system has the problem of artifacts as shown in the ultrasound image displayed on the first page.
Wilson teaches means for image recognition and automatic calculation of muscle and fat composition by charting the brightness of columns in a rotated ultrasound image. The middle of bands of bright column regions is assumed to be an interface. The wide bands result in obvious inaccuracies because the interface is not directly determined. The fat layers are not parallel to the bottom of the longissimus dorsi muscle. When Wilson looks at columns parallel to the fat layers, he will obtain artifacts and a large bright region for the bottom of the Longissimus dorsi because that interface will not be parallel to the columns.
Labor costs and inconsistent grading are significant problems in the meat processing industry. Other attempts have been made to automate the grading and inspection systems involved in meat processing. For example see U.S. Pat. No. 4,931,933, entitled, "Application of Knowledge-Based System for Grading Meat" granted to Chen et al, and U.S. Pat. No. 5,079,951, entitled "Ultrasonic Carcass Inspection" granted to Raymond et al. However, these systems are overly complicated and do not provide an efficient method of accurately measuring the Longissimus dorsi muscle depth and fat composition.
Berlow et al. [1989] developed computer software at Cornell University to measure fat depth, rib eye area, and marbling from ultrasonic images of beef using threshold brightness levels. The results were encouraging: fat depth estimates were quite satisfactory compared with physical carcass measurements (r=0.92) while the correlations between computer measured rib eye area and marbling and their corresponding carcass measurements were not as high as expected (r=0.47 for area and r=0.36 for marbling). The author found that considerable variation in correlations existed among different breed populations. Overall image brightness levels vary with each image and can be adjusted by the operator for best viewing. So an arbitrary threshold level is not at all practical for a variety of operators and varying populations of subjects.
Very few studies on computer processing of ultrasonic images from animals have been available. The results seen in the literature were not as satisfactory or consistent as might have been expected. Incorporating machines into the evaluating process has been studied. However, there has been no accurate and efficient system developed yet for practical use.
The intramuscular fat or marbling is the most important Quality Grade factor associated with beef in the U.S. and some other countries. Unfortunately, it is difficult (if not impossible) to visually assess marbling in a live animal and it is subjective to do so in an animal carcass. Producers desire a tool for classifying beef animals for marbling in feedlots and packers demand a means of sorting beef hot carcasses for marbling in abattoirs. Several previous studies [Lake, 1991; Aneshansley et al., 1990; Brethour, 1990; Berlow et al., 1989; Thane et al., 1989] indicate that ultrasound has the potential to meet both requirements. Currently, the interest in utilizing ultrasonic equipment is increasing [Lake, 1991; Stouffer 1991].
In particular for marbling evaluation, the characteristics of the ultrasound speckle and its image texture are not yet completely understood. The results of relating some parameters obtained from the image to its corresponding carcass marbling score are inconsistent.
Berlow et al [1989] attempted to use image threshold technique to estimate marbling scores but the correlations achieved were very low (r=0.34 to 0.37). Thane et al [1989] associated the analyzed image parameters such as the Fourier distance, fractal dimension and slope of attenuation, with marbling score and the results showed correlation coefficients from 0.03 to 0.36. On the other hand, Brethour [1990] demonstrated a visual scoring system based on the observed level of speckle over the longissimus dorsi (1.d.) muscle, the echogenicity of the rib bone, and speckle pattern below the rib bone as an estimate of marbling. This system classified beef carcasses as Select or Choice grades with 77% accuracy. Several earlier investigators [Wagner et al., 1983; Flax et al., 1981; Burckhardt, 1978] of ultrasound B-scanning have used stochastic signal analysis to describe the character of ultrasound speckle as coherent noise and demonstrated that the speckle noise is autocorrelated. Very little work, however, has been devoted to connect the autocorrelation property of ultrasonic speckle with beef marbling score.
The speckle noise of ultrasound is a complicated function of the roughness of surface which reflects or scatters ultrasonic waves and such noise is autocorrelated. It can be argued that the animal tissue can be treated as a collection of scatterers. It is believed that the tissue of longissimus muscle with many small particles of intramuscular fat or marbling will be rougher and thereby have more scatterers than those without marbling. It is, therefore, reasonable to speculate that there exists a certain relationship between the speckle autocorrelation and marbling.
Longissimus dorsi or ribeye muscle cross-sectional area is currently obtained by manually tracing around the perceived outline of the muscle from an ultrasonic image. Some ultrasonic scanners, like the latest model we have been using [Aloka, 1990a], provide the capability of approximating the area with an ellipse. Due to its low degree of accuracy and the relatively large time requirement, this feature is seldom used. It is, however, more common for the images to be recorded on a video tape and the area analysis done at a later time. This analysis is still a very time consuming process. Because of the quality of the image, accurately tracing the 1.d. muscle area can be done only by trained technicians. It is, therefore, very difficult to achieve efficiency, consistency and accuracy.
In previous studies attempts were made to develop computer software to automate the ribeye area tracing process. Namely, Betlow et al. [1989a] developed a three-target-point algorithm for ultrasonic images from beef and showed that the correlations between the computer and manual 1.d. muscle area measurements were in the range from 0.055 to 0.47, depending upon the image quality and the cattle breeds. McDonald and Chen [1989] presented a modified version of the Meyer algorithm [1979] to isolate the 1.d. region from a photographic image of the exposed surface of a beef carcass rib. The method achieved adequate performance under certain circumstances, but it is not suitable for ultrasonic images. Lake [1991] reported results of research conducted in Australia about computer aided tracing of the 1.d. muscle, but the tracing process per se is done manually with a pointing device.
The present invention includes the discovery of the problems described herein and their solutions.